
# import neccessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px


# upload data:
df = pd.read_csv("F:\\archive (14)\\Instagram data.csv")


# To see the top five heads of the df
df.head(2)


# To See the last five rows of data:
df.tail(2)


# To see the shape of data
df.shape

(119, 13)


# To see the size of data
df.size

1547


# To check the dtypes of data, there is all integers except caption and Hashtags:
df.dtypes

Impressions        int64
From Home          int64
From Hashtags      int64
From Explore       int64
From Other         int64
Saves              int64
Comments           int64
Shares             int64
Likes              int64
Profile Visits     int64
Follows            int64
Caption           object
Hashtags          object
dtype: object


# To get an infomation of data:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 119 entries, 0 to 118
Data columns (total 13 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Impressions     119 non-null    int64 
 1   From Home       119 non-null    int64 
 2   From Hashtags   119 non-null    int64 
 3   From Explore    119 non-null    int64 
 4   From Other      119 non-null    int64 
 5   Saves           119 non-null    int64 
 6   Comments        119 non-null    int64 
 7   Shares          119 non-null    int64 
 8   Likes           119 non-null    int64 
 9   Profile Visits  119 non-null    int64 
 10  Follows         119 non-null    int64 
 11  Caption         119 non-null    object
 12  Hashtags        119 non-null    object
dtypes: int64(11), object(2)
memory usage: 12.2+ KB


# To see all the columns in the data:
df.columns

Index(['Impressions', 'From Home', 'From Hashtags', 'From Explore',
       'From Other', 'Saves', 'Comments', 'Shares', 'Likes', 'Profile Visits',
       'Follows', 'Caption', 'Hashtags'],
      dtype='object')


# To get a statistical summary of data:
df.describe().T


# To check the null values in the data or not, there is no null values so treatment is not required;
df.isnull().sum()

Impressions       0
From Home         0
From Hashtags     0
From Explore      0
From Other        0
Saves             0
Comments          0
Shares            0
Likes             0
Profile Visits    0
Follows           0
Caption           0
Hashtags          0
dtype: int64


# see the value counts of data with unstack
df.value_counts(ascending=True).unstack()


# see unique values in the data
df.nunique()

Impressions       101
From Home          97
From Hashtags     100
From Explore       95
From Other         84
Saves              84
Comments           15
Shares             28
Likes              85
Profile Visits     59
Follows            29
Caption            90
Hashtags           54
dtype: int64


# To get the two columns together to do comparison:
df[['Impressions', 'Likes']]


# make a relational plot in which x axis contains value of impressions and y axis contains values of likes,
# this plot telling us the relationship between impressions and Likes, how they are connected with each 
# other ......there is more likes more impressions and less likes then getting less impressions,
sns.relplot(data= df, x="Impressions", y="Likes")

<seaborn.axisgrid.FacetGrid at 0x1beaddee2e0>


# To get a groupby of impressions and from home values:
df.groupby(['Impressions', 'From Home']).sum().T


# To see relational plot of impressions and from home which is telling us the relationship of both,
# more impressions when posts from home and the getting low...
sns.relplot(data= df, x="Impressions", y="From Home")

<seaborn.axisgrid.FacetGrid at 0x1beadf261f0>


# Relational plot of impressions and 'from explore' values of data which is telling us their relationship
sns.relplot(data= df, x="Impressions", y="From Explore")

<seaborn.axisgrid.FacetGrid at 0x1beae700730>


# Relational plot of impressions and 'from other ', which telling us their relationship 
sns.relplot(data= df, x="Impressions", y="From Other")

<seaborn.axisgrid.FacetGrid at 0x1beae76e280>


# to get the group by impressions with sum values of all varaiables with see the heads:
df.groupby('Impressions').sum().head(2).T


# KDE is plot of 'Impressions'
sns.kdeplot(df['Impressions'], shade = True)

<AxesSubplot:xlabel='Impressions', ylabel='Density'>


# Blank Cell


# Pairplot of data:
sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x1beae7c8940>


# See the correlation of data
corr= df.corr()


# To see the relationship between the variables:
corr


# Make Heat map of variables to check the higher and lower values 
# Draw the heatmap which shows the relationship between the varaibles how they are related to each other,
# dark blocks shows higher values(strongly related) , medium color shows medium values and light color blocks shows lesser
#(weak relation)
# values..

plt.figure(figsize=(12,6))
sns.heatmap(corr, annot= True, cmap= 'coolwarm')

<AxesSubplot:>


# make a pivot table for Impressions and From home variables:
table = pd.pivot_table(data=df,index=['Impressions','From Home']).mean()
table

Comments             6.352941
Follows             22.823529
From Explore      1178.568627
From Hashtags     1968.284314
From Other         184.549020
Likes              176.823529
Profile Visits      54.666667
Saves              156.549020
Shares               9.303922
dtype: float64


# By these two variables we are analyzing the reach of my instagram posts,i will look at the distribution
# of impressions i have received from home, this graph is telling us how much impressions values are 
# receiveing from the varible "From Home", how much it is influencing the 'Impressions' for the posts.
plt.figure(figsize=(10, 8))
plt.style.use('fivethirtyeight')
plt.title("Distribution of Impressions From Home")
sns.distplot(df['From Home'])
plt.show()

# summary: this impressions we getting from the home section on instagram which shows how much my posts 
# reach my followers, by seeing this impressions from home, i can say it's hard to reach all my followers 
# daily.

C:\Users\Simmy\anaconda3\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


# making a pivot table for the "Impressions" and " From Hashtags" variables, which will further
# playing a role in distribution graph for these two and analyzing the reach of the instagram posts.
table = pd.pivot_table(data=df,index=['Impressions','From Hashtags']).mean()
table

Comments             6.352941
Follows             22.823529
From Explore      1178.568627
From Home         2496.911765
From Other         184.549020
Likes              176.823529
Profile Visits      54.666667
Saves              156.549020
Shares               9.303922
dtype: float64


# By these two variables we are analyzing the reach of my instagram posts,i will look at the distribution
# of impressions i have received from home, this graph is telling us how much impressions values are 
# receiveing from the variable "From Hshtags", how much it is influencing the 'Impressions' for the posts
# by the "From Hashtags" variable...
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Hashtags")
sns.distplot(df['From Hashtags'])
plt.show()

#summary: now lets have a look on this distribution of impressions i received from hashtags, by seeing this 
# graph i m analyzing that impressions are stronger by hashtags section later on , it is falling down,
# Hashtags are tools we use to categorize our posts on Instagram so that we can reach more people based
# on the kind of content we are creating. Looking at hashtags impressions shows that not all posts can be 
# reached using hashtags, but many new users can be reached from hastags.not all posts connected to
# hashtags but some of the posts connected to hashtags therefore, some of the posts reaching to users,
# this is why , initially it is raising and influencing "Impressions" and then later it is falling , now
# impressions are falling.

C:\Users\Simmy\anaconda3\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


# making a pivot table for the "Impressions" and " From Explore" variables, which will further
# playing  a role in  distribution graph for these two and analyzing the reach of the instagram posts.
table = pd.pivot_table(data=df,index=['Impressions','From Explore']).mean()
table

Comments             6.352941
Follows             22.823529
From Hashtags     1968.284314
From Home         2496.911765
From Other         184.549020
Likes              176.823529
Profile Visits      54.666667
Saves              156.549020
Shares               9.303922
dtype: float64


# By these two variables we are analyzing the reach of my instagram posts,i will look at the distribution
# of impressions i have received from home, this graph is telling us how much impressions values are 
# receiveing from the variable "From Explore", how much it is influencing the 'Impressions' for the posts
# by the "From Explore" variable...
plt.figure(figsize=(10,8))
plt.title("Distribution of Impressions From Explore")
sns.distplot(df['From Explore'])
plt.show()

#summary: Now lets have a look on distribjution graph of "impressions" and "From Explore", by this
#explore section of instagram is the reccomendation system of instagram.It recommends posts to the users
#based on their preferences and interests.By seeing at the impressions i have received from the explore 
# section, i can say that instagram doest not reccomend our posts much to mthe users.Some posts have
# reach from the explore section, but still very low as compared to the reach i recieve from hastags.

C:\Users\Simmy\anaconda3\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


# making a pivot table for the "Impressions" and " From Other" variables, which will further
# playing  a role in  distribution graph for these two and analyzing the reach of the instagram posts.
table = pd.pivot_table(data=df,index=['Impressions','From Other']).mean()
table

Comments             6.352941
Follows             22.823529
From Explore      1178.568627
From Hashtags     1968.284314
From Home         2496.911765
Likes              176.823529
Profile Visits      54.666667
Saves              156.549020
Shares               9.303922
dtype: float64


# making a distribution plot of "Impressions" and "From Other". By these two variables we are analyzing the reach of my instagram posts,i will look at the distribution
# of impressions i have received from home, this graph is telling us how much impressions values are 
# receiveing from the variable "From Other", how much it is influencing the 'Impressions' for the posts
# by the "From Other" variable...
plt.figure(figsize=(10,8))
plt.style.use('fivethirtyeight')
plt.title("Distribution of Impressions From Other")
sns.distplot(df['From Other'])
plt.show()

# summary: From other section , posts are reaching to the users by the other resources, like other links,
# advertisement, frens of frens, followers of follwers or their following or in other ways, by seeing at r
# the other section ,some posts are reaching from the other section but it is very low as compared to other 
# sections, it inflencing the impressions but in very low.

C:\Users\Simmy\anaconda3\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


#Now let’s have a look at the percentage of impressions I get from various sources on Instagram, making a 
# donut chart to get better understanding of all sections which is required for the analyses.
home = df["From Home"].sum()
hashtags = df["From Hashtags"].sum()
explore = df["From Explore"].sum()
other = df["From Other"].sum()

labels = ['From Home','From Hashtags','From Explore','Other']
values = [home, hashtags, explore, other]

fig = px.pie(df, values=values, names=labels, 
             title='Impressions on Instagram Posts From Various Sources', hole=0.5)
fig.show()

#summary: So the above donut plot shows that almost 50 per cent of the reach is from my followers, 
#38.1 per cent is from hashtags, 9.14 per cent is from the explore section, 
#and 3.01 per cent is from other sources.


#Let’s have a look at the relationship between the number of likes and the number of
#impressions on my Instagram posts:
figure = px.scatter(data_frame= df, x = "Impressions",
                   y = "Likes", size= "Likes",trendline = 'ols',
                   title= "Relationship Between Likes and Impresssions")
figure.show()
#There is a linear relationship between the number of likes and the reach I got on Instagram.


#Now let’s see the relationship between the number of comments and the number of impressions on 
#my Instagram posts:
figure = px.scatter(data_frame= df, x = "Impressions", y= "Comments", size = "Comments",trendline= 'ols',
                   title= "Relationship Between Impressions and Comments")
figure.show()
#It looks like the number of comments we get on a post doesn’t affect its reach.


#Now let’s have a look at the relationship between the number of shares and the number of impressions:
figure = px.scatter(data_frame= df, x="Impressions", y = "Shares",trendline= "ols",
                   title= "Relationship Between Impressions and Shares", size= "Shares")
figure.show()
#A more number of shares will result in a higher reach, but shares don’t affect the reach of a post
#as much as likes do.


#Now let’s have a look at the relationship between the number of saves and the number of impressions:
figure = px.scatter(data_frame= df, x="Impressions", y = "Saves",trendline= "ols",
                   title= "Relationship Between Impressions and Shares", size= "Saves")
figure.show()
#There is a linear relationship between the number of times my post is 
#saved and the reach of my Instagram post.


#Now let’s have a look at the relationship between the number of impressions and the follows:
figure = px.scatter(data_frame= df, x="Impressions", y = "Follows",trendline= "ols",
                   title= "Relationship Between Impressions and Shares", size= "Follows")
figure.show()


# find out the correlation between the varibles.
correlation = df.corr()
print(correlation["Impressions"].sort_values(ascending = False))

Impressions       1.000000
From Explore      0.893607
Follows           0.889363
Likes             0.849835
From Home         0.844698
Saves             0.779231
Profile Visits    0.760981
Shares            0.634675
From Other        0.592960
From Hashtags     0.560760
Comments         -0.028524
Name: Impressions, dtype: float64


# correlation of likes columns with sorting with decending order:
print (correlation["Likes"].sort_values(ascending = False))

Likes             1.000000
Impressions       0.849835
Saves             0.845643
Follows           0.746333
Shares            0.707794
From Home         0.698330
From Hashtags     0.662124
From Explore      0.653699
Profile Visits    0.626107
From Other        0.393510
Comments          0.123586
Name: Likes, dtype: float64


# sorting values of some variables:
df.sort_values(by=['Likes','Follows'], inplace=True,
               ascending = [True, True])
print(df)

     Impressions  From Home  From Hashtags  From Explore  From Other  Saves  \
19          2407       1338            655           276          39     40   
38          2191       1308            809            45          18     35   
86          2407       1338            655           276          39     40   
6           2621       1543            599           333          25     22   
20          2064       1304            362           249          37     49   
..           ...        ...            ...           ...         ...    ...   
114        13700       5185           3041          5352          77    573   
40         16062       3144          11817           564         468    252   
107        17396       1817          10008          5192         251    285   
118        36919      13473           4176         16444        2547    653   
117        32695      11815           3147         17414         170   1095   

     Comments  Shares  Likes  Profile Visits  Follows  \
19          8      20     72              10        0   
38          2       1     72              18        0   
86          8      20     72              10        0   
6           5       1     76              26        0   
20          4       5     76               9        0   
..        ...     ...    ...             ...      ...   
114         2      38    373              73       80   
40          6      20    416             330       94   
107         7       7    416             467      260   
118         5      26    443             611      228   
117         2      75    549             148      214   

                                               Caption  \
19   Data Science Use Cases: Here�s how Zomato is u...   
38   Language detection is a natural language proce...   
86   Data Science Use Cases: Here�s how Zomato is u...   
6    Learn how to analyze a candlestick chart as a ...   
20   A boxplot is a statistical data visualization ...   
..                                                 ...   
114  Here are some of the best data science certifi...   
40   280 Machine Learning Projects Solved & Explain...   
107  Here is a list of 100+ Machine Learning Algori...   
118  175 Python Projects with Source Code solved an...   
117  Here are some of the best data science certifi...   

                                              Hashtags  
19   #data�#datascience�#dataanalysis�#dataanalytic...  
38   #data�#datascience�#dataanalysis�#dataanalytic...  
86   #data�#datascience�#dataanalysis�#dataanalytic...  
6    #stockmarket�#investing�#stocks�#trading�#mone...  
20   #datavisualization�#datascience�#data�#dataana...  
..                                                 ...  
114  #datascience�#datasciencejobs�#datasciencetrai...  
40   #data�#datascience�#dataanalysis�#dataanalytic...  
107  #machinelearning�#machinelearningalgorithms�#d...  
118  #python�#pythonprogramming�#pythonprojects�#py...  
117  #datascience�#datasciencejobs�#datasciencetrai...  

[119 rows x 13 columns]


# Analyzing the conversion rate:
#In Instagram, conversation rate means how many followers you are getting from the number of 
#profile visits from a post. The formula that you can use to calculate conversion rate is 
#(Follows/Profile Visits) * 100.
#Now let’s have a look at the conversation rate of my Instagram account:

conversion_rate= (df["Follows"].sum()/df["Profile Visits"].sum()*100)
conversion_rate

41.00265604249668


# Blank Cell:----------------------*************


#home = df["From Home"].sum()
#hashtags = df["From Hashtags"].mean()
#explore = df["From Explore"].mean()
#other = df["From Other"].mean()

#labels = ['From Home','From Hashtags','From Explore','Other']
#values = [home, hashtags, explore, other]

#fig = px.pie(df, values=values, names=labels, 
             #title='Impressions on Instagram Posts From Various Sources', hole=0.5)
#fig.show()


#figure = px.scatter(data_frame= df, x = 'Profile Visits', 
                   #y = "Follows", size = "Follows", trendline= "ols", 
                   #title = "Relationship Between Profile Visits and Followers Gained")
#figure.show()


#df.groupby(['Saves', 'Comments']).sum()


#df.groupby('Impressions').agg({'From Home': ['mean', 'min', 'max']})


#****END PROJ

	count	mean	std	min	25%	50%	75%	max
Impressions	119.0	5703.991597	4843.780105	1941.0	3467.0	4289.0	6138.0	36919.0
From Home	119.0	2475.789916	1489.386348	1133.0	1945.0	2207.0	2602.5	13473.0
From Hashtags	119.0	1887.512605	1884.361443	116.0	726.0	1278.0	2363.5	11817.0
From Explore	119.0	1078.100840	2613.026132	0.0	157.5	326.0	689.5	17414.0
From Other	119.0	171.092437	289.431031	9.0	38.0	74.0	196.0	2547.0
Saves	119.0	153.310924	156.317731	22.0	65.0	109.0	169.0	1095.0
Comments	119.0	6.663866	3.544576	0.0	4.0	6.0	8.0	19.0
Shares	119.0	9.361345	10.089205	0.0	3.0	6.0	13.5	75.0
Likes	119.0	173.781513	82.378947	72.0	121.5	151.0	204.0	549.0
Profile Visits	119.0	50.621849	87.088402	4.0	15.0	23.0	42.0	611.0
Follows	119.0	20.756303	40.921580	0.0	4.0	8.0	18.0	260.0

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Impressions	From Home	From Hashtags	From Explore	From Other	Saves	Comments	Shares	Likes	Profile Visits	Follows	Caption
1941	1466	411	37	17	49	6	3	82	8	2	A neural network is a computational structure that connects an input layer to an output layer. Here�s how a neural network works.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2064	1304	362	249	37	49	4	5	76	9	0	A boxplot is a statistical data visualization technique for analyzing the distribution and patterns of numerical data points of a dataset. It represents quartile 1, quartile 3, median, maximum and minimum data points of a feature which helps to understand the distribution of the numerical values of a dataset. Here�s how to analyze box plots as a Data Scientist.	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2191	1308	809	45	18	35	2	1	72	18	0	Language detection is a natural language processing task where we need to identify the language of a text or document. Here you will learn how to train a machine learning model for language detection using Python.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2218	1597	411	162	15	28	6	3	81	29	4	Learn the implementation of AlexNet Convolutional Neural Network using Python. Link in Bio	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2327	1774	435	59	35	45	3	3	85	7	2	Here are all the databases that are used by Facebook.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16062	3144	11817	564	468	252	6	20	416	330	94	280 Machine Learning Projects Solved & Explained using Python programming language: Link in Bio	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
17396	1817	10008	5192	251	285	7	7	416	467	260	Here is a list of 100+ Machine Learning Algorithms and Models explained using Python that will help you to understand the implementation of all the machine learning algorithms and models in solving real-time business problems. You can find this list of 100+ Machine Learning Algorithms and Models from the link in bio.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
17713	2449	2141	12389	561	504	3	23	308	70	96	Here are some of the best resources to learn SQL for data science.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN
32695	11815	3147	17414	170	1095	2	75	549	148	214	Here are some of the best data science certifications that you can choose from in 2022.	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
36919	13473	4176	16444	2547	653	5	26	443	611	228	175 Python Projects with Source Code solved and explained for free: Link in Bio	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Impressions	From Home	From Hashtags	From Explore	From Other	Saves	Comments	Shares	Likes	Profile Visits	Follows
Impressions	1.000000	0.844698	0.560760	0.893607	0.592960	0.779231	-0.028524	0.634675	0.849835	0.760981	0.889363
From Home	0.844698	1.000000	0.177516	0.800573	0.555666	0.768817	0.012716	0.674985	0.698330	0.531076	0.672675
From Hashtags	0.560760	0.177516	1.000000	0.190453	0.229623	0.305929	0.161439	0.219511	0.662124	0.691345	0.555485
From Explore	0.893607	0.800573	0.190453	1.000000	0.495685	0.747803	-0.158565	0.615731	0.653699	0.531850	0.796019
From Other	0.592960	0.555666	0.229623	0.495685	1.000000	0.331907	-0.108703	0.156834	0.393510	0.633080	0.546737
Saves	0.779231	0.768817	0.305929	0.747803	0.331907	1.000000	-0.026912	0.860324	0.845643	0.360628	0.628461
Comments	-0.028524	0.012716	0.161439	-0.158565	-0.108703	-0.026912	1.000000	0.016933	0.123586	0.096714	-0.060631
Shares	0.634675	0.674985	0.219511	0.615731	0.156834	0.860324	0.016933	1.000000	0.707794	0.245361	0.493070
Likes	0.849835	0.698330	0.662124	0.653699	0.393510	0.845643	0.123586	0.707794	1.000000	0.626107	0.746333
Profile Visits	0.760981	0.531076	0.691345	0.531850	0.633080	0.360628	0.096714	0.245361	0.626107	1.000000	0.853152
Follows	0.889363	0.672675	0.555485	0.796019	0.546737	0.628461	-0.060631	0.493070	0.746333	0.853152	1.000000

	Impressions	From Home	From Hashtags	From Explore	From Other	Saves	Comments	Shares	Likes	Profile Visits	Follows	Caption	Hashtags
0	3920	2586	1028	619	56	98	9	5	162	35	2	Here are some of the most important data visua...	#finance�#money�#business�#investing�#investme...
1	5394	2727	1838	1174	78	194	7	14	224	48	10	Here are some of the best data science project...	#healthcare�#health�#covid�#data�#datascience�...

	Impressions	Likes
0	3920	162
1	5394	224
2	4021	131
3	4528	213
4	2518	123
...	...	...
114	13700	373
115	5731	148
116	4139	92
117	32695	549
118	36919	443